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The Drag on Efficiency: Why Legacy Data Warehouses and SQL are Hampering Batch Processes in Indian Banks



In today's competitive Indian banking landscape, data-driven decision making is no longer a luxury, it's a necessity. Banks need to leverage the power of their data to innovate offerings, optimize operations, ensure compliance, personalize customer experiences, and mitigate risks. However, many institutions are held back by outdated data infrastructure, specifically reliance on traditional SQL-based data warehouses or historic Hadoop infrastructure. This article will explore how legacy data warehouses and SQL are hindering batch processing efficiency, impacting banks' ability to unlock the true value of their data.


The Burden of Batch Processing:

Batch processing remains a cornerstone for many banking operations. Daily or weekly processes ingest, transform, and load massive datasets to populate data warehouses for analysis. These processes are crucial for tasks like:

  • Regulatory Reporting: Banks are subject to a multitude of regulatory requirements, demanding timely and accurate reporting. Batch processes ensure compliance by gathering and aggregating data for reports. These reports (Batch processes) often aggregate data across CBS, LMS, LOS, Treasury, NPA, Payments and other systems. 

  • Operational Reporting: Departments within the Bank need data from various systems to analyse performance, track trends and drive operations. Whether its profitability by product or customer complaints by region or adoption of digital banking, data is the source and needs to be sliced and diced for various reports and dashboards which are created using Batch processes. 

  • Customer Segmentation: Understanding customer behavior allows banks to tailor products and services. Batch processing helps segment customers based on demographics, transactions, and other factors.

  • Fraud Detection: Identifying fraudulent activities, preventing money laundering etc. are critical for financial security. Batch processing historical data helps create fraud detection models and analyze anomalies.


The Bottlenecks of Legacy Systems:

While batch processing serves a purpose, traditional data warehouses and SQL create significant bottlenecks:

  • Scalability Limitations: Legacy data warehouses typically rely on relational databases, which struggle to handle the ever-increasing volume and variety of data generated by modern banking operations. This translates to slow processing times and limits the ability to incorporate new data sources.

  • Schema Rigidity: Traditional data warehouses have predefined schemas that are often inflexible. Adding new data fields or adapting to evolving business needs requires complex schema modifications, delaying batch processing cycles.

  • Limited Processing Power: SQL, while a powerful language, isn't optimized for large-scale data processing. Batch jobs written in SQL can be slow and inefficient, especially when dealing with complex transformations or aggregations.

  • High Cost: Data Warehouse platforms were not prepared for an era of data overload / rapid digital transformation. So semi structured data types like JSON, machine logs, unstructured data like KYC docs & images have all had to find a way into these old gen platforms which has resulted in cost bloating both for Infrastructure and license. 


The Impact on Indian Banks:

These limitations of legacy data infrastructure have a direct impact on Indian banks:

  • Delayed Insights: Inefficient batch processes lead to longer turnaround times for data analysis. This delays critical decision making and hinders banks' ability to react to market trends or customer needs in real-time.

  • Increased Costs: Slow processing times necessitate longer hardware usage, increasing operational costs. Additionally, the need for specialized skills to manage complex data warehouse architectures adds to the financial burden.

  • Compliance Challenges: Delayed batch processing can lead to missed deadlines for regulatory reporting, potentially resulting in fines and penalties.


The Modernization Imperative:

To overcome these challenges, Indian banks require a data stack modernization strategy. Here's what it entails:

  • Embracing Modern Data Formats: Moving from a warehouse mindset to a lake house mindset is needed to address requirements of today. Modern data storage formats (Iceberg, Deltatable, Parquet, Avro) offer scalability, flexibility, and cost optimization. Unlike traditional formats, Iceberg guarantees data consistency through ACID (Atomicity, Consistency, Isolation, Durability) transactions. This ensures data integrity during large-scale batch updates, crucial for financial data.

  • Cloud Adoption:  Cloud can handle diverse data formats and provide on-demand resources for efficient batch processing. Choosing architecture elements like Spark, Kafka, Iceberg ensures ability to scale across commodity hardware on prem or on the cloud or hybrid, making it ideal for handling ever-growing banking datasets.

  • Leveraging Big Data Technologies: Tools like Apache Spark provide parallel processing capabilities, significantly accelerating batch jobs. Spark can handle complex transformations and aggregations in-memory, drastically reducing processing times. Spark is significantly faster than older frameworks like Hadoop MapReduce. This is due to its in-memory processing capabilities. By storing data in RAM, Spark can perform calculations much quicker than constantly reading and writing from disk.

  • Adopting New Data Management Techniques: Techniques like data lakes offer a centralized repository for raw data, allowing for flexible schema and future-proofing the data infrastructure. Iceberg allows for seamless schema changes without impacting existing data or requiring costly table rewrites. This flexibility allows banks to incorporate new data sources, like social media sentiment analysis, without disrupting existing batch processes.

  • Adhering to Compliance needs: Regulatory compliance is paramount for banks. Iceberg's data versioning capabilities enable banks to "travel back in time" and audit historical data states. This proves invaluable for ensuring regulatory adherence and troubleshooting potential data errors. 


The Benefits of Modernization:

Modernizing the data stack unlocks significant benefits for Indian banks:

  • Faster Batch Processing: Modern architecture and big data tools ensure efficient data processing, enabling quicker turnaround times for data analysis and reporting.

  • Improved Data Quality: Modern data management practices ensure data accuracy and consistency, leading to more reliable insights for decision making.

  • Enhanced Agility: A flexible and scalable data infrastructure allows banks to adapt to changing business needs and integrate new data sources seamlessly.

  • Reduced Costs: Modernization can lead to cost savings through efficient resource utilization, reduced hardware needs, and streamlined data management processes.


Conclusion:

Legacy data warehouses, Hadoop and SQL are hindering the efficiency of batch processing, impacting the ability of Indian banks to unlock the power of their data. Modernizing the data stack with cloud-based data warehouses, big data technologies, and new data management techniques is essential for faster processing, improved data quality, and enhanced agility. Drona Pay has created a modular open source data stack that has helped banks modernise their data architecture and batch processing. The Drona Pay Modern Data Stack provides scalable data ingestion, processing, storage, governance and visualization built on top of leading open source elements including Iceberg, Debezium, Kafka, Airflow, Spark and Superset. By embracing these advancements, Indian banks can gain a competitive edge by leveraging the full potential of data-driven decision making.


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